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Published December 14, 2004 | public
Journal Article

Learning and Measuring Specialization in Collaborative Swarm Systems


This paper addresses qualitative and quantitative diversity and specialization issues in the framework of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies correlation between the swarm's heterogeneity with its overall performance. Starting from the stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investigate quantitatively the influence of task constraints and types of reinforcement signals on performance, diversity, and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication among agents the swarm becomes specialized after learning. The degrees of both diversity and specialization are affected strongly by environmental conditions and task constraints. While the specialization measure reveals characteristics related to performance and learning in a clearer way than diversity does, the latter measure appears to be less sensitive to different noise conditions and learning parameters.

Additional Information

© 2004 by International Society of Adaptive Behavior. First Published December 1, 2004. We would like to thank Carl Anderson, Tucker Balch, Christopher Cianci, and the anonymous reviewers, for their valuable input and suggestions. This work has been principally supported by the Caltech Center for Neuromorphic Systems Engineering under the US NSF Cooperative Agreement EEC-9402726 and the Northrop Grumman Corporation Foundation. Alcherio Martinoli is currently sponsored by a Swiss NSF professorship.

Additional details

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October 20, 2023